Early prediction of gestational diabetes: a practical model combining clinical and biochemical markers
Bibliographic record
Abstract
BACKGROUND: Gestational diabetes (GDM) is usually diagnosed late in pregnancy, precluding early preventive interventions. This study aims to develop a predictive model based on clinical factors and selected biochemical markers for the early risk assessment of GDM. METHODS: Based on a prospective cohort of 7929 pregnant women from the Quebec City metropolitan area, a nested case-control study was performed including 264 women who developed GDM. Each woman who developed GDM was matched with two women with normal glycemic profile. Risk prediction models for GDM and GDM requiring insulin therapy were developed using multivariable logistic regression analyses, based on clinical characteristics and the measurement of three clinically validated biomarkers: glycated hemoglobin (HbA1c), sex hormone binding globulin (SHBG) and high-sensitivity C-reactive protein (hsCRP) measured between 14 and 17 weeks of gestation. RESULTS: HbA1c and hsCRP were higher and SHBG was lower in women who developed GDM (p<0.001). The selected model for the prediction of GDM, based on HbA1c, SHBG, BMI, past history of GDM, family history of diabetes and soft drink intake before pregnancy yielded an area under the ROC curve (AUC) of 0.79 (0.75-0.83). For the prediction of GDM requiring insulin therapy, the selected model including the same six variables yielded an AUC of 0.88 (0.84-0.92) and a sensitivity of 68.9% at a false-positive rate of 10%. CONCLUSIONS: A simple model based on clinical characteristics and biomarkers available early in pregnancy could allow the identification of women at risk of developing GDM, especially GDM requiring insulin therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".